In the credit scoring task, we have only ground-truth default/non-default labels for approved samples but no ground-truth default/non-default labels for rejected samples.
This causes the missing-not-at-random bias in data [1]}[2]}[3]} for machine learning models. Some reject inference approaches are accordingly proposed [4]}[5]}[6]}.
